activity
20212023
most citedDeep Learning Based Cloud Cover Parameterization for ICON

67 citations · 208 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2023★ 19 cited

ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation

Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…

physics.ao-ph2023★ 43 cited

Causally-informed deep learning to improve climate models and projections

Fernando Iglesias-Suarez, Pierre Gentine, Breixo Solino-Fernandez +4

Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the r…

physics.ao-ph2023★ 29 cited

Data-Driven Equation Discovery of a Cloud Cover Parameterization

Arthur Grundner, Tom Beucler, Pierre Gentine +1

A promising method for improving the representation of clouds in climate models, and hence climate projections, is to develop machine learning-based parameterizations using output…

physics.ao-ph2022★ 16 cited

Machine-learned cloud classes from satellite data for process-oriented climate model evaluation

A. Kaps, A. Lauer, G. Camps-Valls +3

Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks…

physics.ao-ph2022★ 34 cited

Non-Linear Dimensionality Reduction with a Variational Encoder Decoder to Understand Convective Processes in Climate Models

Gunnar Behrens, Tom Beucler, Pierre Gentine +3

Deep learning can accurately represent sub-grid-scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack…

physics.ao-ph2021★ 67 cited

Deep Learning Based Cloud Cover Parameterization for ICON

Arthur Grundner, Tom Beucler, Pierre Gentine +3

A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm-reso…

cs.LG2021

On the Generalization of Agricultural Drought Classification from Climate Data

Julia Gottfriedsen, Max Berrendorf, Pierre Gentine +4

Climate change is expected to increase the likelihood of drought events, with severe implications for food security. Unlike other natural disasters, droughts have a slow onset and…